A September 2024 study from Imperial College London shows how zero-knowledge proofs (ZKPs) can help companies verify that their machine learning (ML) models treat all demographic groups equally while still keeping model details and user data private. Zero-knowledge proofs are cryptographic methods that enable one party to prove to another that a statement is true without revealing any additional information beyond the statement’s validity.
It is not optimizing for outcomes — only that every demographic should have the same opportunity to get a job or a home loan. Likewise, predictive equality measures if an ML model makes predictions with the same accuracy across various demographics, so no one is penalized simply for being part of a group. In both cases, the ML model is not putting its thumb on the scale for equity reasons but only to ensure that groups are not being discriminated against in any way.
Zero-knowledge proofs can serve as an airtight way to verify ML models are doing this without revealing private data. While ZKPs have faced plenty of scalability challenges over the years, the technology is finally becoming affordable for mainstream use cases.
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Author / Journalist: Cointelegraph by Rob Viglione
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